Bolleman et al. Journal of Biomedical Semantics (2016) 7:39
DOI 10.1186/s13326-016-0067-z
RESEARCH
Open Access
FALDO: a semantic standard for
describing the location of nucleotide and
protein feature annotation
Jerven T. Bolleman1* , Christopher J. Mungall2 , Francesco Strozzi3 , Joachim Baran4 , Michel Dumontier5 ,
Raoul J. P. Bonnal6 , Robert Buels7 , Robert Hoehndorf8 , Takatomo Fujisawa9 , Toshiaki Katayama10
and Peter J. A. Cock11
Abstract
Background: Nucleotide and protein sequence feature annotations are essential to understand biology on the
genomic, transcriptomic, and proteomic level. Using Semantic Web technologies to query biological annotations,
there was no standard that described this potentially complex location information as subject-predicate-object triples.
Description: We have developed an ontology, the Feature Annotation Location Description Ontology (FALDO), to
describe the positions of annotated features on linear and circular sequences. FALDO can be used to describe
nucleotide features in sequence records, protein annotations, and glycan binding sites, among other features in
coordinate systems of the aforementioned “omics” areas. Using the same data format to represent sequence positions
that are independent of file formats allows us to integrate sequence data from multiple sources and data types. The
genome browser JBrowse is used to demonstrate accessing multiple SPARQL endpoints to display genomic feature
annotations, as well as protein annotations from UniProt mapped to genomic locations.
Conclusions: Our ontology allows users to uniformly describe – and potentially merge – sequence annotations from
multiple sources. Data sources using FALDO can prospectively be retrieved using federalised SPARQL queries against
public SPARQL endpoints and/or local private triple stores.
Keywords: SPARQL, RDF, Semantic Web, Standardisation, Sequence ontology, Annotation, Data integration,
Sequence feature
Background
Describing regions of biological sequences is a vital part
of genome and protein sequence annotation, and in areas
beyond this such as describing modifications related
to DNA methylation or glycosylation of proteins. Such
regions range from one amino acid (e.g. phosphorylation
sites in singalling cascades) to multi megabase contigs
mapped to a complete genome. Such annotation has been
discussed in biological literature since at least 1949 [1] and
recorded in biological databases since the first issue of the
Atlas of Protein Sequence and Structure [2] in 1965.
*Correspondence: [email protected]
1 Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical
Universitaire, 1 rue Michel, Servet, 1211 Geneva 4, Switzerland
Full list of author information is available at the end of the article
There are many different conventions for storing
genomic data and its annotations in plain text flat file formats such as Generic Feature Format version 3 (GFF3),
Genome Variation Format (GVF) [3], Gene Transfer Format (GTF) and Variant Call Format (VCF), and more
structured domain specific formats such as those from
INSDC (International Nucleotide Sequence Database
Collaboration) or UniProt, but none are flexible enough
to discuss all aspects of genetics or proteomics. Furthermore, the fundamental designs of these formats are
inconsistent, for example both zero-based and one-based
counting standards exist, a regular source of off-by-one
programming errors, which experienced bioinformaticians learn to look out for.
© 2016 Bolleman et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Bolleman et al. Journal of Biomedical Semantics (2016) 7:39
Although non-trivial, file format interconversion is a
common background task in current script-centric bioinformatics pipelines, often essential for combining tools
supporting different formats or format variants. As a
result of this common need, file format parsing is a particular strength of community developed open source bioinformatics libraries like BioPerl [4], Biopython [5], BioRuby
[6] and BioJava [7]. While using such shared libraries can
reduce the programmer time spent dealing with different file formats, adopting Semantic Web technologies has
even greater potential to simplify data integration tasks.
As part of the Integrated Database Project (http://
lifesciencedb.mext.go.jp/en/) and the Core Technology Development Program (http://biosciencedbc.jp/en/
33-en/programs/236-programs) to integrate life science
databases in Japan, the National Bioscience Database Center (NBDC) and the Database Center for Life Science
(DBCLS) have hosted an annual “BioHackathon” series
of meetings bringing together biological database teams,
open source programmers, and domain experts in Semantic Web and Linked Data [8–11]. At these meetings it
was recognised that failure to standardise how to describe
positions and regions on biological sequences would be
an obstacle to the adoption of federalised SPARQL Protocol and RDF Query Language (SPARQL) queries which
have the potential to enable cross-database queries and
analyses. Discussion and prototyping with representatives from major sequence databases such as UniProt
[12], DDBJ (DNA Data Bank of Japan) [13] (part of the
INSDC partnership with the National Center for Biotechnology Information (NCBI)-GenBank [14] and European
Molecular Biology Laboratory (EMBL)-Bank [15]), and a
number of glycomics databases (BCSDB [16], GlycomeDB
[17], GLYCOSCIENCES.de [18], JCGGDB, RINGS [19]
and UniCarbKB [20]) and assorted open source developers during these meetings led to the development of
the Feature Annotation Location Description Ontology
(FALDO).
FALDO has been designed to be general enough to
describe the position of annotations on nucleotide and
protein sequences using the various levels of location
complexity used in major databases such as INSDC
(DDBJ, NCBI-GenBank and EMBL-Bank) and UniProt,
their associated file formats, and other generic annotation
file formats such as Browser Extensible Data (BED), GTF
and GFF3. It includes compound locations, which are the
combination of several regions (such as the ‘join’ location
string in INSDC), as well as ambiguous positions. It allows
us to accurately describe ambiguous positions today in
such a way that future more precise knowledge does not
introduce logical conflicts, which potentially could only be
resolved by intervention of an expert in the field.
FALDO is suited to accurately describe the position of
a feature on multiple sequences. This is expected to be
Page 2 of 12
most useful when lifting annotation from one draft assembly version to another. For example, a gene can start at a
position for a given species’ genome assembly, while the
conceptually same gene can start at another position in
previous/following genome assemblies for the species in
question.
FALDO has a deliberately narrow scope which does not
address general annotation issues about the meaning of
or evidence for a location, rather FALDO is intended be
used in combination with other relevant ontologies such
as the Sequence Ontology (SO) [21] or database-specific
ontologies. That is, it is used only to describe the loci
of features, not to describe the features themselves. A
FALDO position relative to a sequence record is comparable to a coordinate position on a map: it makes no claim
about how that sequence record or map is related to the
real world.
Implementation
FALDO is a small web ontology language version 2
(OWL2) ontology with 16 classes, 11 of these deal with
the concept of a position on a sequence (Fig. 1). The
instances of the faldo : ExactPosition represent
positions that are accurately determined in respect to a
reference sequence. There are two convenience subclasses
of faldo : ExactPosition to represent positions on
the N and C-terminal of a amino acid sequence. Three
of those classes are used to describe accurately what we
know of a position that is not precisely determined. Four
classes are used to describe the concept of a position on a
strand of DNA, e.g. positive, negative and on both strands.
All ten of these classes are sub classes of the generic
faldo : Position super-class. The eleventh class is the
concept of a region i.e. something with a end and start
position. The remaining 3 classes are used to group
regions which are biologically related but for which no
exact semantics are available e.g. some legacy data sources
cannot be mapped cleanly without expert intervention.
In contrast to other representations, FALDO has no
explicit way to say that it is not “known” on which strand
a position is, because this explicit statement unknown
strand position can introduce contradictions when merging different data sets. For example, some positions could
end up being contradictorily typed both as forwardstranded as well as being located on an unknown strand
position.
There are 3 more classes (faldo: CollectionOfRegions
and its subclasses) that are only there for backwards
compatibility with INSDC join features with uncertain semantics. i.e. those join regions where a conversion program can only state that there are some
regions and that the order that they are declared in
the INSDC record might have biological significance.
However, here the INSDC record needs intelligent
Bolleman et al. Journal of Biomedical Semantics (2016) 7:39
Page 3 of 12
Fig. 1 The classes and object properties used in FALDO
inspection before the data can be cleanly converted to a
data model with rich semantics.
FALDO defines a single datatype property,
faldo : position, that is used to provide a onebased integer offset from the start of a reference
sequence. This property, when used together with
the faldo : reference property, links the concept of a faldo : Position to an instance of a
biological sequence. Note that these terms are casesensitive: faldo : position is a property, and
faldo : Position is a concept.
For compatibility with a wide range of data, FALDO
makes very few assumptions about the representation of the reference sequence, and can be used to
describe positions on both single- and double-stranded
sequences. When both strands of a double-stranded
sequence are represented by a single entity (recommended
over each strand being represented separately), integer
faldo : position properties are counted from the 5’
end of whichever strand is considered the “forward”
strand.
A key part of the FALDO model is the separation of feature and where a feature is found in a sequence record. For
this we use the faldo : location object property. This
property is used to distinguish between a conceptual gene
as an “unit of inheritance” and the corresponding representation of the DNA sequence region encoding the gene
as stored in a database.
As in the INSDC data model and the associated GenBank ASN.1 notation, each location in FALDO has an
identifier for the sequence it is found on [22]. This means
that the position information is complete without further
references to the context the position information was
found in. The difference is that in FALDO, due to its RDF
nature, the identifier of the sequence is a dereferencable pointer (URI) on the web, instead of just a string of
characters.
Figure 2 shows how FALDO can be used to describe the
position of features on a sequence, and compares it to the
INSDC and GFF3/GTF text orientated formats.
Easier data integration due to OWL reasoning
Two owl : Classes ease data-integration with a owl :
hasKey construct. A faldo : ExactPosition is
the same as another position if it has the same
faldo : position and faldo : reference. In practicse this means that if two sequence records are declared
to be owl : sameAs then the features mapped to one of
these sequence records is automatically mapped to the
other. i.e. One extra statement allows feature annotation
from a UniProt protein record to be transferred to INSDC
Coding Domain Features.
Compression via OWL2 reasoning
For large databases such as INSDC or UniProt, the need
to repeat the reference sequence for each position may
Bolleman et al. Journal of Biomedical Semantics (2016) 7:39
Page 4 of 12
Fig. 2 Assorted conventions for regions, start, end, and strands. This figure shows two hypothetical features on a DNA sequence (labeled chr1), on
either the forward strand (orange) or reverse strand (blue). Using the INSDC location string notation, these regions are “1050..2080” and
“complement(1050..2080)” respectively if implicitly given in terms of the reference chr1. Using the GTF/GFF3 family of formats, regardless of
the strand these two locations are described with start = 1050 and end = 2080, and in general, start ≤ end. Biologically speaking, in terms of
transcription, the start of a genomic feature is strand dependent. For the forward strand feature (orange), the start is 1050 while the reverse strand
feature (blue) starts from 2080
come with a significant cost in storage. However, this
triple does not need to be materialised in the database,
as it is inferrable using OWL2 property chain reasoning.
With the axiom shown in Fig. 3 the faldo : reference
triples can be inferred for any faldo : position
described by an INSDC record. Having an OWL-capable
query rewriter allows users to ignore the difference
between encoding the faldo : reference properties
explicitly and having them inferred at query time. For
RDF databases that do not offer this capability, the necessary triples can be easily added using a single SPARQL
insert query (Fig. 4). This flexibility allows users of the
data to select the best approach for their infrastructure,
rather than being constrained by the decisions of the data
provider.
sequence. When added to an UniProt record in RDF, this
extra RDF annotation would be ignored by applications
that are not concerned with glycosylation of isoforms. The
same annotation cannot be added to UniProt XML as the
XSD schema does not allow for it, and the older plain
text flat-file format does not allow for this kind of third
party extension either. An attempt to add such information would very likely break any XML or flat-file parser
and introduces the risk of importing data incorrectly. Only
the UniProt RDF format allows other people to make
assertions about UniProt data without breaking existing
tools.
There are many ways to add constraints to the data
model by applications using Semantic Web technologies
[23]. In other words, data validation is an application
specific concern instead of a data format concern.
Validating data encoded with FALDO
Some databases only allow a subset of FALDO. For example INSDC requires that the start and end of a region
are on the same sequence, while UniProt requires that a
feature is described in relation to the reference’s canonical isoform. Yet another database might annotate the
location of a glycsoylation site on an UniProt isoform
Users
FALDO is already deployed and used in a number of tools
and databases, in each case extended with more semantic
web data using resource specific ontologies and schemas
as well as other semantic standards e.g. the Sequence
Ontology.
Fig. 3 OWL2 property chain axiom to infer that all positions described in an INSDC record are relative to the main sequence of the record (in RDF
turtle syntax, prefixes omitted)
Bolleman et al. Journal of Biomedical Semantics (2016) 7:39
Page 5 of 12
Fig. 4 A SPARQL query to add all faldo : reference properties to faldo : positions described from a insdc : record
GFVO The Genomic Feature and Variation Ontology
(GFVO) uses FALDO to describe loci on genomic
landmarks as well as individual genomic feature
positions [24].
JBrowse JBrowse can use SPARQL queries with FALDO
to visualize annotations on reference sequences from
semantic databases [25] (see Fig. 5).
INSDC-DDBJ DDBJ is currently working on an RDF
format for the INSDC data that is stored in
DDBJ/GenBank/EMBL-Bank.
BioInterchange BioInterchange makes use of FALDO
in its RDF formatted output to describe genomic
position information stored in the bioinformatics file
formats of GFF3, GTF, GVF and VCF (http://www.
codamono.com/biointerchange).
TogoGenome TogoGenome is a genome database collection provided by the DBCLS that uses FALDO in its
RDF representation (http://togogenome.org/).
PhenomeBrowser The positions on the mouse genome
of phenotype and disease related natural variations
are described using FALDO.
BOING The “bio-ontology integrated querying of
sequence annotations” framework uses FALDO to
describe all feature locations [26].
Fig. 5 JBrowse showing features, whose location is encoded using FALDO, selected via SPARQL (at e.g. http://togogenome.org/gene/1016998:
SPAB_00296)
Bolleman et al. Journal of Biomedical Semantics (2016) 7:39
SPARQL-BED This simple tool that turns any BED
file into a Web accessible SPARQL endpoint using
FALDO to describe BED feature positions (https://
github.com/JervenBolleman/sparql-bed).
BioPerl BioPerl [4] now includes a FALDO exporter
(Bio::FeatureIO::faldo), which allows any
BioPerl-supported feature format to be translated to
FALDO.
UniProt UniProt annotates many protein features and
sites. Starting with UniProt RDF release 2014_01 the
positions of protein feature are described using FALDO.
Results
One of the practical goals driving the development of
FALDO was to be able to represent all the annotated
sequences in INSDC and UniProt as RDF triples, as a step
towards providing this data via SPARQL endpoints where
it can be queried.
The protein examples considered here, such as the
UniProt feature annotations, describe relatively simple
Page 6 of 12
locations within protein sequences (see the active site
annotation in Figs. 6 and 7).
Complement strand
Describing biological features in relation to a genomic
DNA sequence does not have to be complicated.
For example the cheY gene (shown in Fig. 8)
Escherichia coli str. K-12 substr. MG1655 (accession
NC_000913.2) is described in the INSDC feature
table as complement(1965072..1965461), which
is 390 base pairs using inclusive one-based counting.
This feature begins on the base complementary to
start = 1965461 and finishes at end = 1965072,
so the INSDC location string can be interpreted as
complement(end..start). FALDO respects this biological interpretation of a feature location on the reverse
strand.
In contrast, other formats such as the GFF family of
formats, require start ≤ end regardless of the strand,
which is equivalent to interpreting the INSDC location
Fig. 6 Excerpt from UniProt entry Q6Q250 showing the position of an active site and a signal peptide in both the UniProt flat-file format and FALDO
Bolleman et al. Journal of Biomedical Semantics (2016) 7:39
Page 7 of 12
Fig. 7 DDBJ record associated with UniProt Q6Q250 showing the related CDS sequence, with coding region outside of the known deposited mRNA
sequence
Fig. 8 Using FALDO in Turtle [29] syntax to describe the location of a gene feature cheY at complement(NC_000913.2:1965072..
1965461) in the INSDC record U00096.3
Bolleman et al. Journal of Biomedical Semantics (2016) 7:39
string as complement(start..end). This convention has
some practical advantages when dealing with numerical
operations on features sets, such as checking for overlaps
or indexing data. For example, the feature length is given
by length = end − start + 1 under this numerically convenient scheme where the interpretation of start versus end
is strand independent.
INSDC compound locations
There are a number of implicit conventions in INSDC
data that would ideally translated into a more explicit
model when using FALDO. However, to enable automated bulk conversion of existing data, the FALDO class
faldo : CollectionOfRegions and its subclasses
exist to describe the compound locations used in the
INSDC feature tables. Specifically, join(...) locations
where the order is known map to FALDO’s faldo :
ListOfRegions, while order(...) where the
order is unknown map to to faldo : BagOfRegions.
Page 8 of 12
Thus while gene models with an intron/exon structure
can be described this way, it is preferable when converting
to RDF to explicitly describe the individual exons, each of
which would have a simple location in FALDO.
One special case of INSDC compound regions is features on a circular chromosome that overlap the chromosome’s origin of replication. One such feature is the
“Protein II” gene from the reverse strand of f1 bacteriophage (ddbj:J02448). “Protein II” transcription starts at
position 6006 on the reverse strand and ends at position
831 (see Fig. 9).
Fuzzy locations
Feature positions in, for example, INSDC or UniProt,
are not always exactly known or described, but we
should strive to describe our limited knowledge as accurately as possible. Take for example the position of the
signal peptide annotation shown in Fig. 6, where the
protein sequence is known to belong to a family of
Fig. 9 Partial example of using FALDO in JSON-LD [30] syntax to describe the CDS “Protein II” at join(6006..6407,1..831) on J02448.
Notice that this is given as a single location rather than being artificially split in two as in the INSDC join(...) notation
Bolleman et al. Journal of Biomedical Semantics (2016) 7:39
proteins, but unfortunately only a part of the amino
acid sequence is known. The UniProt curator deduced
that the signal peptide region only partly overlaps the
known sequence fragment. The same is true in the related
INSDC record, were the CDS starts and ends before the
known mRNA sequence (see Fig. 7). As demonstrated
in the figure, this limited knowledge can be described
using the FALDO classes faldo : InRangePosition
and faldo : OneOfPosition.
Restriction enzymes
The task of describing the recognition sites of most
restriction enzymes is quite straightforward, as is describing the cleavage site of a blunt end cutting enzyme.
However, the cut site of a sticky-end cutting enzyme
like HindIII that leaves an “overhang” is more challenging to specify, since it cuts in a different place
on the forward and reverse strands. Figure 10 demonstrates how to describe this in FALDO by specifying start
and end positions of the cut site that are on different
strands.
Page 9 of 12
Discussion
When designing FALDO, a broad range of use cases
were considered from human genome annotations to
protein domains and glycan binding sites on amino acid
sequences, with the goal of developing a scheme general
enough to describe regions of DNA, RNA and protein
sequences.
Advantages and drawbacks of existing file formats were
considered, including line based column formats like BED
and GTF/GFF3, which focus on exact ranges on a given
sequence, and the more complex locations supported by
the INSDC feature tables used by DDBJ, NCBI-GenBank
and EMBL-Bank.
The simplest non-stranded range location on a linear
sequence requires a start and end coordinate, but even
here there are existing competing conventions for describing open or closed end-points using zero and one-based
counting (for example BED versus GTF/GFF3/INSDC).
In FALDO we always count from the start of the forward 5’–3’ strand, even for features on the reverse strand.
This encoding means there is no need to know the length
Fig. 10 FALDO representation of the HindIII restriction enzyme cleavage site with sticky ends
Bolleman et al. Journal of Biomedical Semantics (2016) 7:39
of the sequence to compare positions on the different
strands of a linear chromosome or genome. The end
and start position of a region is inclusive. Unlike formats like GTF/GFF3, FALDO shares with Chado [27] the
convention that the start coordinate should be the biological start (which may be a numerically higher value than
the end coordinate).
For a semantic description describing the strand explicitly is preferable. FALDO chooses to add the strand
information to the position. This is required to accurately describe for example the sticky ends of an enzyme
digestion cut site, as in the HindIII example (Fig. 10).
A major difference with other standards is that we chose
to make strandedness and reference sequence a property
of the position, instead of the region. This is important
in a number of use cases. For example, one may need
to describe the position of a gene on a draft genome
assembly where the start and end are known to be on
different contigs. This can be the case when RNA mapping is used in the genome assembly process. Another is
when rough semantics are used in queries e.g. answering what is the start and end of a gene. In a process
called transplicing, exons of one gene can be found on
multiple chromosomes, or on different strands of the
same chromosome. e.g. gene mod(mdg4) of Drosophila
melanogaster (uniprot:Q86B87). In such cases the start
of the gene can be on a different reference sequence
or strand than the end. These biological realities cannot
be described accurately if the reference sequence was a
property of the region. As a side effect, it allows single
nucleotide or amino acid sites to be described directly as
a position without a need for an artificial region of length
one.
Every faldo : Position refers to the sequence it is
on. This allows us to say that gene XX starts at position 4 of assembly Y 1, while the same conceptual gene
starts at position 5 of assembly Y 2. Even within the same
assembly, FALDO offers the possibility to describe features in different contexts at the same time, allowing for
instance to represent a SNP in terms of its position within
a known coding region (i.e. gene coordinates) and within
a chromosome region, which offers clear advantages for
features annotation. Chado also allows multiple locations
per feature, but unlike FALDO, the start and end of any
location must be in the same region, which prohibits
for example a feature that spans more than one contig,
or describing the same feature on two different genome
assemblies.
Efficiency of region-of-interest queries
For FALDO we also considered query efficiency in comparison to existing search technology. Region of interest
(ROI) queries are common operations performed on a set
of genome annotations to extract a set of features within
Page 10 of 12
a range. For applications such as genome browsers, it is
important that these are efficient enough. Although some
RDF query engines may perform poorly when performing ROI queries over large feature sets, others have special
indexes (e.g. literal filter indexes) that improve query performance. There is scope for further optimisation in the
context of a SPARQL query by combining efficient algorithms and indexes such as Nested Containment Lists
(NCLs) [28] or spatial indexes.
As a RDF based format, FALDO can be used to represent feature position information in a wide variety
of serialisations e.g. JSON-LD, RDF/XML, Turtle, RDFa
(embedded in HTML). This allows developers flexibility in consideration of their usage scenario, while at the
same time allowing conversion to the common RDF triple
model used in RDF databases and accessed by SPARQL
queries.
Conclusions
FALDO is a small ontology for describing biological
features in a consistent manner that bioinformaticians
can depend upon. The diverse software and high-profile
databases already using FALDO show that it has enough
power to describe existing biological feature locations.
The uptake of this ontology means that it is now much
easier for users querying biological databases on the
Semantic Web to compare features on the basis of locations. This also means that visualisation tools that access
positional data via SPARQL can easily reuse significant
parts of queries between databases.
Abbreviations
BED: browser extensible data (file format); DDBJ: DNA data bank of Japan;
EMBL: European molecular biology laboratory; FALDO: feature annotation
location description ontology; GFF: generic feature format; GFF3: generic
feature format version 3; GTF: gene transfer format, a variant of GFF; GVF:
genome variation format, an extension to GFF3; INSDC: international
nucleotide sequence database collaboration; OWL2: web ontology language
(note acronym is OWL, not WOL); RDF: resource description framework;
SPARQL: SPARQL protocol and RDF query language; UniProtKB: universal
protein knowledgebase; VCF: variant call format.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
JTB wrote the basic ontology file and mapping to UniProt RDF and
contributed to the text of this article. CM added the Bio :: FeatureIO ::
faldo logic to BioPerl and contributed to the OWL modeling. FS contributed
to the Cufflinks RDF converter and wrote the VCF converter that uses FALDO
for sequence variations positions. JB incorporated FALDO into the
BioInterchange software as well as the Genomic Feature and Variation
Ontology (GFVO). MD worked on the general modelling as well as mapping
FALDO to SIO as an upper ontology. RJPB adapted BioRuby to match the
ontology and wrote the Cufflinks to locations.rdf converter. RB adapted
JBrowse to query SPARQL endpoints that use this format to generate custom
tracks. RH did the GFF3 to OWL conversion. TF and TK implemented an
ontology and a tool for converting INSDC records to RDF and used FALDO to
describe features on genomes in TogoGenome. PC wrote a large section of
this paper, and co-ordinated the working group during the BioHackathon
meetings. All authors read and approved the final manuscript.
Bolleman et al. Journal of Biomedical Semantics (2016) 7:39
Acknowledgments
The developers recognize the invaluable contributions from the community in
helping to create this standard. We would like to especially thank the
organisers and funders of the BioHackathon series of meetings for hosting the
original discussions leading to FALDO (http://www.biohackathon.org/). The
BioHackathon series, this work and Toshiaki Katayama were supported by
National Bioscience Database Center (NBDC) of Japan Science and Technology
Agency (JST) and Database Center for Life Science (DBCLS) of Research
Organization of Information and Systems (ROIS) in Japan. Jerven Bolleman was
supported in his role at Swiss-Prot, whose activities at the SIB Swiss Institute of
Bioinformatics are supported by the Swiss Federal Government through the
The State Secretariat for Education, Research and Innovation SERI. Christopher
Mungall was supported by the NIH under R24OD011883 and by the Director,
Office of Science, Office of Basic Energy Sciences, of the U.S. Department of
Energy under Contract No. DE-AC02-05CH11231. Peter Cock was supported
by the Scottish Government Rural and Environmental Research and Analysis
Directorate. Takatomo Fujisawa was supported by the DNA Databank of Japan
(DDBJ), Research Organization of Information and Systems (ROIS) in Japan.
Availability and requirements
FALDO is publicly available at the URL http://biohackathon.org/resource/faldo
which is developed under source code control at https://github.com/
JervenBolleman/FALDO hosted by GitHub Inc, where everyone is free to
suggest extensions and improvements and if required extend FALDO to meet
their unique requirements. FALDO currently uses the Creative Commons
Attribution Zero 1.0 Public Domain dedication license, making FALDO
available to use and reuse free of charge.
The ontology is shared in the Turtle (http://www.w3.org/TR/turtle/) RDF
syntax, which can be automatically converted to another RDF syntax such as
RDF/XML if required.
Author details
1 Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical
Universitaire, 1 rue Michel, Servet, 1211 Geneva 4, Switzerland. 2 Genomics
Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, US.
3 CeRSA, Parco Tecnologico Padano, Lodi 26900, Italy. 4 CODAMONO, 5-121
Marion Street, Toronto, Ontario M6R 1E6, Canada. 5 Stanford Center for
Biomedical Informatics Research, 1265 Welch Road, Room X223, Stanford, CA
94305-5479, US. 6 Integrative Biology Program, Istituto Nazionale Genetica
Molecolare, Milan, Italy. 7 University of California, Berkeley, Berkeley, CA, USA.
8 Department of Computer Science, Aberystwyth SY23 3DB, UK. 9 Center for
Information Biology, National Institute of Genetics, Research Organization of
Information and Systems, 1111 Yata, Mishima, Shizuoka 411-08540, Japan.
10 Database Center for Life Science, Research Organization of Information and
Systems, 2-11-16, Yayoi, Bunkyo-ku, Tokyo, 113-0032, Japan. 11 The James
Hutton Institute, Dundee DD2 5DA, UK.
Page 11 of 12
7.
8.
9.
10.
11.
Received: 5 February 2014 Accepted: 17 March 2016
References
1. Sanger F. The terminal peptides of insulin. Biochem J. 1949;45(5):563–74.
2. Dayhoff MO, Eck RV, Foundation NBR. Atlas of Protein Sequence and
Structure. Silver Spring (Maryland): National Biomedical Research
Foundation; 1965.
3. Reese MG, Moore B, Batchelor C, Salas F, Cunningham F, Marth GT,
Stein L, Flicek P, Yandell M, Eilbeck K. A standard variation file format for
human genome sequences. Genome Biol. 2010;11(R88):.
doi:10.1186/gb-2010-11-8-r88.
4. Stajich JE, Block D, Boulez K, Brenner SE, Chervitz SA, Dagdigian C,
Fuellen G, Gilbert JGR, Korf I, Lapp H, Lehväslaiho H, Matsalla C,
Mungall CJ, Osborne BI, Pocock MR, Schattner P, Senger M, Stein LD,
Stupka E, Wilkinson MD, Birney E. The Bioperl toolkit: Perl modules for the
life sciences. Genome Res. 2002;12(10):1611–8. doi:10.1101/gr.361602.
5. Cock PJA, Antao T, Chang JT, Chapman BA, Cox CJ, Dalke A,
Friedberg I, Hamelryck T, Kauff F, Wilczynski B, de Hoon MJL. Biopython:
freely available Python tools for computational molecular biology and
bioinformatics. Bioinformatics. 2009;25(11):1422–3. doi:10.1093/
bioinformatics/btp163.
6. Goto N, Prins P, Nakao M, Bonnal R, Aerts J, Katayama T. BioRuby:
bioinformatics software for the Ruby programming language.
Bioinformatics. 2010;26(20):2617–9. doi:10.1093/bioinformatics/btq475.
12.
13.
14.
Prlić A, Yates A, Bliven SE, Rose PW, Jacobsen J, Troshin PV, Chapman M,
Gao J, Koh CH, Foisy S, Holland R, Rimsa G, Heuer ML,
Brandstätter-Müller H, Bourne PE, Willis S. BioJava: an open-source
framework for bioinformatics in 2012. Bioinformatics. 2012;28(20):2693–5.
doi:10.1093/bioinformatics/bts494.
Katayama T, Arakawa K, Nakao M, Ono K, Aoki-Kinoshita KF,
Yamamoto Y, Yamaguchi A, Kawashima S, Chun HW, Aerts J, Aranda B,
Barboza LH, Bonnal RJ, Bruskiewich R, Bryne JC, Fernández JM,
Funahashi A, Gordon PM, Goto N, Groscurth A, Gutteridge A, Holland R,
Kano Y, Kawas EA, Kerhornou A, Kibukawa E, Kinjo AR, Kuhn M, Lapp H,
Lehvaslaiho H, Nakamura H, Nakamura Y, Nishizawa T, Nobata C,
Noguchi T, Oinn TM, Okamoto S, Owen S, Pafilis E, Pocock M, Prins P,
Ranzinger R, Reisinger F, Salwinski L, Schreiber M, Senger M,
Shigemoto Y, Standley DM, Sugawara H, Tashiro T, Trelles O, Vos RA,
Wilkinson MD, York W, Zmasek CM, Asai K, Takagi T. The DBCLS
BioHackathon: standardization and interoperability for bioinformatics
web services and workflows. J Biomed Semantics. 2010;1:8.
doi:10.1186/2041-1480-1-8.
Katayama T, Wilkinson MD, Vos R, Kawashima T, Kawashima S, Nakao M,
Yamamoto Y, Chun HW, Yamaguchi A, Kawano S, Aerts J,
Aoki-Kinoshita KF, Arakawa K, Aranda B, Bonnal RJ, Fernández JM,
Fujisawa T, Gordon PM, Goto N, Haider S, Harris T, Hatakeyama T, Ho I,
Itoh M, Kasprzyk A, Kido N, Kim YJ, Kinjo AR, Konishi F, Kovarskaya Y,
von Kuster G, Labarga A, Limviphuvadh V, McCarthy L, Nakamura Y,
Nam Y, Nishida K, Nishimura K, Nishizawa T, Ogishima S, Oinn T,
Okamoto S, Okuda S, Ono K, Oshita K, Park KJ, Putnam N, Senger M,
Severin J, Shigemoto Y, Sugawara H, Taylor J, Trelles O, Yamasaki C,
Yamashita R, Satoh N, Takagi T. The 2nd DBCLS BioHackathon:
interoperable bioinformatics Web services for integrated applications.
J Biomed Semantics. 2011;2:6. doi:10.1186/2041-1480-2-4.
Katayama T, Wilkinson MD, Micklem G, Kawashima S, Yamaguchi A,
Nakao M, Yamamoto Y, Okamoto S, Oouchida K, Chun HW, Aerts J,
Afzal H, Antezana E, Arakawa K, Aranda B, Belleau F, Bolleman J,
Bonnal RJP, Chapman B, Cock PJA, Eriksson T, Gordon PMK, Goto N,
Hayashi K, Horn H, Ishiwata R, Kaminuma E, Kasprzyk A, Kawaji H,
Kido N, Kim YJ, Kinjo AR, Konishi F, Kwon KH, Labarga A, Lamprecht AL,
Lin Y, Lindenbaum P, McCarthy L, Morita H, Murakami K, Nagao K,
Nishida K, Nishimura K, Nishizawa T, Ogishima S, Ono K, Oshita K,
Park KJ, Prins P, Saito TL, Samwald M, Satagopam VP, Shigemoto Y,
Smith R, Splendiani A, Sugawara H, Taylor J, Vos RA, Withers D,
Yamasaki C, Zmasek CM, Kawamoto S, Okubo K, Asai K, Takagi T. The
3rd DBCLS BioHackathon: improving life science data integration with
Semantic Web technologies. J Biomed Semantics. 2013;4:6.
doi:10.1186/2041-1480-4-6.
Katayama T, Wilkinson MD, Aoki-Kinoshita KF, Kawashima S,
Yamamoto Y, Yamaguchi A, Okamoto S, Kawano S, Kim JD, Wang Y,
Wu H, Kano Y, Ono H, Bono H, Kocbek S, Aerts J, Akune Y, Antezana E,
Arakawa K, Aranda B, Baran J, Bolleman J, Bonnal RJP, Buttigieg PL,
Campbell MP, Chen Y-A, Chiba H, Cock PJA, Cohen KB, Constantin A,
Duck G, Dumontier M, Fujisawa T, Fujiwara T, Goto N, Hoehndorf R,
Igarashi Y, Itaya H, Ito M, Iwasaki W, Kalaš M, Katoda T, Kim T,
Kokubu A, Komiyama Y, Kotera M, Laibe C, Lapp H, Lütteke T,
Marshall MS, Mori T, Mori H, Morita M, Murakami K, Nakao M,
Narimatsu H, Nishide H, Nishimura Y, Nystrom-Persson J, Ogishima S,
Okamura Y, Okuda S, Oshita K, Packer NH, Prins P, Ranzinger R,
Rocca-Serra P, Sansone S, Sawaki H, Shin SH, Splendiani A, Strozzi F,
Tadaka S, Toukach P, Uchiyama I, Umezaki M, Vos R, Whetzel PL,
Yamada I, Yamasaki C, Yamashita R, York WS, Zmasek CM, Kawamoto S,
Takagi T. Biohackathon series in 2011 and 2012: penetration of ontology
and Linked Data in life science domains.
J Biomed Semantics. 2014;5:5. doi:10.1186/2041-1480-5-5.
The UniProt Consortium. Update on activities at the Universal Protein
Resource (UniProt) in 2013. Nucleic Acids Res. 2013;41(D1):43–7.
doi:10.1093/nar/gks1068. http://nar.oxfordjournals.org/content/41/D1/
D43.full.pdf+html.
Ogasawara O, Mashima J, Kodama Y, Kaminuma E, Nakamura Y, Okubo
K, Takagi T. Ddbj new system and service refactoring. Nucleic Acids Res.
2013;41(D1):25–9. doi:10.1093/nar/gks1152. http://nar.oxfordjournals.org/
content/41/D1/D25.full.pdf+html.
Benson DA, Cavanaugh M, Clark K, Karsch-Mizrachi I, Lipman DJ,
Ostell J, Sayers EW. Genbank. Nucleic Acids Res. 2013;41(D1):36–42.
Bolleman et al. Journal of Biomedical Semantics (2016) 7:39
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
doi:10.1093/nar/gks1195. http://nar.oxfordjournals.org/content/41/D1/
D36.full.pdf+html.
Cochrane G, Alako B, Amid C, Bower L, Cerdeño-Tárraga A, Cleland I,
Gibson R, Goodgame N, Jang M, Kay S, Leinonen R, Lin X, Lopez R,
McWilliam H, Oisel A, Pakseresht N, Pallreddy S, Park Y, Plaister S,
Radhakrishnan R, Rivière S, Rossello M, Senf A, Silvester N, Smirnov D,
ten Hoopen P, Toribio A, Vaughan D, Zalunin V. Facing growth in the
European Nucleotide Archive. Nucleic Acids Res. 2013;41(D1):30–5.
doi:10.1093/nar/gks1175. http://nar.oxfordjournals.org/content/41/D1/
D30.full.pdf+html.
Toukach PV. Bacterial carbohydrate structure database 3: Principles and
realization. J Chem Inform Modeling. 2011;51(1):159–70.
doi:10.1021/ci100150d. http://pubs.acs.org/doi/pdf/10.1021/ci100150d.
Ranzinger R, Herget S, von der Lieth C-W, Frank M. GlycomeDB – a
unified database for carbohydrate structures. Nucleic Acids Res.
2011;39(suppl 1):373–6. doi:10.1093/nar/gkq1014. http://nar.
oxfordjournals.org/content/39/suppl_1/D373.full.pdf+html.
Lütteke T, Bohne-Lang A, Loss A, Goetz T, Frank M, von der Lieth C-W.
Glycosciences.de: an internet portal to support glycomics and
glycobiology research. Glycobiology. 2006;16(5):71–81.
doi:10.1093/glycob/cwj049. http://glycob.oxfordjournals.org/content/16/
5/71R.full.pdf+html.
Akune Y, Hosoda M, Kaiya S, Shinmachi D, Aoki-Kinoshita KF. The RINGS
resource for glycome informatics analysis and data mining on the web.
OMICS: J Integrative Biol. 2010;14(4):475–86. doi:10.1089/omi.2009.0129.
Campbell MP, Peterson R, Mariethoz J, Gasteiger E, Akune Y,
Aoki-Kinoshita KF, Lisacek F, Packer NH. Unicarbkb: Building a knowledge
platform for glycoproteomics. Underconsideration NAR Databases. 2014.
doi:10.1093/nar/gkt1128.
Eilbeck K, Lewis SE, Mungall CJ, Yandell M, Stein L, Durbin R,
Ashburner M. The Sequence Ontology: A tool for the unification of
genome annotations. Genome Biol. 2005;6(5):44.
doi:10.1186/gb-2005-6-5-r44.
Vakatov D E. Biological sequence data model. Technical report, NCBI.
2013. http://www.ncbi.nlm.nih.gov/toolkit/doc/book/ch_datamod/#
ch_datamod.Locations_on_Biologi.
Le Hors A, Solbrig H, Eric Prud’hommeaux. Rdf validation workshop
report, practical assurances for quality rdf data. Technical report, W3C.
2013. http://www.w3.org/2012/12/rdf-val/report.
Baran J, Durgahee BSB, Eilbeck K, Antezana E, Hoehndorf R, Dumontier
M. GFVO: the Genomic Feature and Variation Ontology. 2015.
PeerJ3:e933. https://doi.org/10.7717/peerj.933.
Skinner ME, Uzilov AV, Stein LD, Mungall CJ, Holmes IH. JBrowse: A
next-generation genome browser. Genome Res. 2009;19:1630–8.
doi:10.1101/gr.094607.109.
Devisscher M, De Meyer T, Van Criekinge W, Dawyndt P. An ontology
based query engine for querying biological sequences. EMBnet J.
2013;19(B):51–5.
Mungall CJ, Emmert DB, The FlyBase Consortium. A Chado case study: an
ontology-based modular schema for representing genome-associated
biological information. Bioinformatics. 2007;23(13):337–46.
doi:10.1093/bioinformatics/btm189. http://bioinformatics.oxfordjournals.
org/content/23/13/i337.full.pdf+html.
Alekseyenko AV, Lee CJ. Nested containment list (NCList): a new algorithm
for accelerating interval query of genome alignment and interval
databases. Bioinformatics. 647. 2007. doi:10.1093/bioinformatics/btl647.
Beckett D, Berners-Lee T, Prud’hommeaux E, Carothers G. Rdf 1.1 turtle terse rdf triple language. Technical report, W3C. 2014. http://www.w3.
org/TR/turtle/.
Sporny M, Kellogg G, Lanthaler M. Json-ld 1.0 - a json-based serialization f
or linked data. Technical report, W3C. 2014. http://www.w3.org/TR/json-ld/.
Page 12 of 12
Submit your next manuscript to BioMed Central
and we will help you at every step:
• We accept pre-submission inquiries
• Our selector tool helps you to find the most relevant journal
• We provide round the clock customer support
• Convenient online submission
• Thorough peer review
• Inclusion in PubMed and all major indexing services
• Maximum visibility for your research
Submit your manuscript at
www.biomedcentral.com/submit